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Record W3174223776 · doi:10.32370/ia_2021_06_10

Problems of Metaphorization in Modern Directing Theater

2021· article· en· W3174223776 on OpenAlexvenueno aff
Irina Ivashchenko

Bibliographic record

VenueIntellectual Archive · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorAppealContext (archaeology)Variety (cybernetics)AestheticsExpression (computer science)Associative propertyComputer scienceSociologyArtLinguisticsHistoryArtificial intelligencePolitical sciencePhilosophyMathematicsLaw

Abstract

fetched live from OpenAlex

Metaphorization as a universal method of replenishing lexical means of expression and creating a complex semantic structure of the production is studied; the types of theatrical metaphors in the context of tendencies of modern directing theater are analyzed; found that primarily metaphorization depends on the goals and objectives of the director, which are focused on filling conceptual gaps and creating a pragmatic effect in the viewer -this leads to predicting understanding of metaphor and appeal to image-associative complexes of current realities.It is revealed that in the modern director's theater there is a process of metaphorization of the surrounding world, in which new metaphorical models appear, and traditional ones are updated and actualized, constantly absorbing new meanings.Within one or another director's theater, there is a variety of activity of appealing to basic metaphors, changing key metaphors and generating new submodels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.017
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.276
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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